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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
14/09/2023 |
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Actualizado : |
14/09/2023 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
REBOLLO, I.; AGUILAR, I.; PÉREZ DE VIDA, F.; MOLINA, F.; GUTIÉRREZ, L.; ROSAS, J.E. |
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Afiliación : |
MARÍA INÉS REBOLLO PANUNCIO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Statistics, University de la República, College of Agriculture, Garzón 780, Montevideo, Montevideo, Uruguay; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO BLAS PEREZ DE VIDA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FEDERICO MOLINA CASELLA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCÍA GUTIÉRREZEPARTMENT OF STATISTICS, UNIVERSITY DE LA REPÚBLICA, COLLEGE OF AGRICULTURE, GARZÓN 780, MONTEVIDEO, MONTEVIDEO, URUGUAY DEPARTMENT OF AGRONOMY, UNIVERSITY OF WISCONSIN–MADISON, 1575 LINDEN DRIVE, MADISON, WI, UNITED STATES, Department of Statistics, University de la República, College of Agriculture, Montevideo, Uruguay; Department of Agronomy, University of Wisconsin-Madison, 1575 Linden Drive, Madison, WI, United States; JUAN EDUARDO ROSAS CAISSIOLS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Statistics, University de la República, College of Agriculture, Garzón 780, Montevideo, Montevideo, Uruguay. |
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Título : |
Genotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations. |
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Complemento del título : |
Original article. |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
Crop Science. 2023, Volume 63, Issue 4, Pages 2220-2240. https://doi.org/10.1002/csc2.21029 -- OPEN ACCESS. |
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ISSN : |
0011-183X (print); 1435-0653 (electronic). |
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DOI : |
10.1002/csc2.21029 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 21 November 2022, Accepted 10 May 2023, Published online 16 June 2023. -- Correspondence: Rosas, J.E.; INIA, Estación Experimental Treinta y Tres, Road 8 km 281, Treinta y Tres, Uruguay; email:jrosas@inia.org.uy -- FUNDING: Funding for this project was provided by Instituto Nacional de Investigación Agropecuaria (Projects AZ35, AZ13, and fellowship to I. R.), Agencia Nacional de Investigación Agropecuaria (grant MOV_CA_2019_1_156241), Comisión Sectorial de Investigación Científica, Universidad de la República (grant Iniciación a la Investgación 2019 No. 8), Comité Académico de Posgrado (fellowship to I. R.), and the Agriculture and Food Research Initiative Competitive Grant 2022-68013-36439 (WheatCAP) from the USDA National Institute of Food and Agriculture. -- LICENSE: This is an open access article under the terms of theCreative Commons Attribution-NonCommercial (http://creativecommons.org/licenses/by-nc/4.0/ ) |
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Contenido : |
ABSTRACT.- Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype-specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega-environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single-step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank-change) GEI with other sowing periods. Climatic variables related to temperature, radiation, wind, and precipitation affecting GY were identified and differed in each population. RRMs with selected climatic covariates improved the predictive ability in both tested and untested years and environments. Prediction using the complete dataset performed better than predicting within each ME. © 2023 The Authors. Crop Science © 2023 Crop Science Society of America. MenosABSTRACT.- Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype-specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega-environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single-step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank-change) GEI with other sowing periods. Climatic variables related to temperature, radiati... Presentar Todo |
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Palabras claves : |
Genotype by environment interaction (GEI); Random regression models (RRMs); Rice (Oryza sativa L.). |
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Asunto categoría : |
-- |
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URL : |
https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.21029
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Marc : |
LEADER 03749naa a2200253 a 4500
001 1064311
005 2023-09-14
008 2023 bl uuuu u00u1 u #d
022 $a0011-183X (print); 1435-0653 (electronic).
024 7 $a10.1002/csc2.21029$2DOI
100 1 $aREBOLLO, I.
245 $aGenotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations.$h[electronic resource]
260 $c2023
500 $aArticle history: Received 21 November 2022, Accepted 10 May 2023, Published online 16 June 2023. -- Correspondence: Rosas, J.E.; INIA, Estación Experimental Treinta y Tres, Road 8 km 281, Treinta y Tres, Uruguay; email:jrosas@inia.org.uy -- FUNDING: Funding for this project was provided by Instituto Nacional de Investigación Agropecuaria (Projects AZ35, AZ13, and fellowship to I. R.), Agencia Nacional de Investigación Agropecuaria (grant MOV_CA_2019_1_156241), Comisión Sectorial de Investigación Científica, Universidad de la República (grant Iniciación a la Investgación 2019 No. 8), Comité Académico de Posgrado (fellowship to I. R.), and the Agriculture and Food Research Initiative Competitive Grant 2022-68013-36439 (WheatCAP) from the USDA National Institute of Food and Agriculture. -- LICENSE: This is an open access article under the terms of theCreative Commons Attribution-NonCommercial (http://creativecommons.org/licenses/by-nc/4.0/ )
520 $aABSTRACT.- Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype-specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega-environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single-step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank-change) GEI with other sowing periods. Climatic variables related to temperature, radiation, wind, and precipitation affecting GY were identified and differed in each population. RRMs with selected climatic covariates improved the predictive ability in both tested and untested years and environments. Prediction using the complete dataset performed better than predicting within each ME. © 2023 The Authors. Crop Science © 2023 Crop Science Society of America.
653 $aGenotype by environment interaction (GEI)
653 $aRandom regression models (RRMs)
653 $aRice (Oryza sativa L.)
700 1 $aAGUILAR, I.
700 1 $aPÉREZ DE VIDA, F.
700 1 $aMOLINA, F.
700 1 $aGUTIÉRREZ, L.
700 1 $aROSAS, J.E.
773 $tCrop Science. 2023, Volume 63, Issue 4, Pages 2220-2240. https://doi.org/10.1002/csc2.21029 -- OPEN ACCESS.
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INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
02/05/2023 |
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Actualizado : |
02/05/2023 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
MACHADO, M.; OLIVEIRA, L.G.S.; SCHILD, C.; BOABAID, F.; LUCAS, M.; BURONI, F.; CASTRO, M. B.; RIET-CORREA, F. |
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Afiliación : |
MIZAEL MACHADO DA COSTA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUIZ GUSTAVO SCHNEIDER DE OLIVEIRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay.; CARLOS SCHILD, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FABIANA BOABAID, Polo de Desarrollo Universitario Del Instituto Superior de La Carne, Centro Universitario Regional (CENUR) Noreste, Universidad de La República, Tacuarembó, Uruguay; MARTÍN LUCAS FONSECA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay.; FLORENCIA BURONI, División de Laboratorios Veterinarios "Miguel C. Rubino" Regional Norte, Ministerio de Ganadería, Agricultura y Pesca (MGAP), Tacuarembó, Uruguay; MÁRCIO B. CASTRO, Veterinary Pathology Laboratory, Veterinary Teaching Hospital, University of Brasilia, Distrito Federal, Brasília, Brazil; FRANKLIN RIET-CORREA AMARAL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Postgraduate Program in Animal Science in the Tropics, Federal University of Bahia, Bahia, Salvador, Brazil. |
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Título : |
Lantana camara poisoning in cattle that took refuge during a storm in a forest invaded by this plant. |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
Toxicon, 2023, Volume 229, article 107124. https://doi.org/10.1016/j.toxicon.2023.107124 |
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ISSN : |
0041-0101 |
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DOI : |
10.1016/j.toxicon.2023.107124 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 8 March 2023; Received in revised form 7 April 2023; Accepted 10 April 2023; Available online 11 April 2023. -- Corresponding author: Riet-Correa, F.; Instituto Nacional de Investigación Agropecuaria, Plataforma de Salud Animal, Estación Experimental del Norte, Tacuarembó, Uruguay, email:franklinrietcorrea@gmail.com -- Handling Editor: Ray Norton -- |
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Contenido : |
An outbreak of poisoning by Lantana camara occurred in cattle when a herd sought refuge in a Eucalyptus forest heavily infested by this plant. The animals showed apathy, elevated serum activities of hepatic enzymes, severe photosensitivity, jaundice, hepatomegaly and nephrosis. After a clinical manifestation period of 2-15 days, 74 out of 170 heifers died. The main histological changes were random hepatocellular necrosis, cholestasis, biliary proliferation and, in one animal, centrilobular necrosis. Immunostaining for Caspase 3 detected scattered apoptotic hepatocytes. © 2023 Published by Elsevier Ltd. |
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Palabras claves : |
Hepatic lesions; Lantana camara; Nephrosis; Photosensitization; PLATAFORMA DE INVESTIGACIÓN EN SALUD ANIMAL; Poisonous plants. |
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Asunto categoría : |
L01 Ganadería |
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Marc : |
LEADER 01940naa a2200313 a 4500
001 1064063
005 2023-05-02
008 2023 bl uuuu u00u1 u #d
022 $a0041-0101
024 7 $a10.1016/j.toxicon.2023.107124$2DOI
100 1 $aMACHADO, M.
245 $aLantana camara poisoning in cattle that took refuge during a storm in a forest invaded by this plant.$h[electronic resource]
260 $c2023
500 $aArticle history: Received 8 March 2023; Received in revised form 7 April 2023; Accepted 10 April 2023; Available online 11 April 2023. -- Corresponding author: Riet-Correa, F.; Instituto Nacional de Investigación Agropecuaria, Plataforma de Salud Animal, Estación Experimental del Norte, Tacuarembó, Uruguay, email:franklinrietcorrea@gmail.com -- Handling Editor: Ray Norton --
520 $aAn outbreak of poisoning by Lantana camara occurred in cattle when a herd sought refuge in a Eucalyptus forest heavily infested by this plant. The animals showed apathy, elevated serum activities of hepatic enzymes, severe photosensitivity, jaundice, hepatomegaly and nephrosis. After a clinical manifestation period of 2-15 days, 74 out of 170 heifers died. The main histological changes were random hepatocellular necrosis, cholestasis, biliary proliferation and, in one animal, centrilobular necrosis. Immunostaining for Caspase 3 detected scattered apoptotic hepatocytes. © 2023 Published by Elsevier Ltd.
653 $aHepatic lesions
653 $aLantana camara
653 $aNephrosis
653 $aPhotosensitization
653 $aPLATAFORMA DE INVESTIGACIÓN EN SALUD ANIMAL
653 $aPoisonous plants
700 1 $aOLIVEIRA, L.G.S.
700 1 $aSCHILD, C.
700 1 $aBOABAID, F.
700 1 $aLUCAS, M.
700 1 $aBURONI, F.
700 1 $aCASTRO, M. B.
700 1 $aRIET-CORREA, F.
773 $tToxicon, 2023, Volume 229, article 107124. https://doi.org/10.1016/j.toxicon.2023.107124
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